Systems and methods of ranking a plurality of credit card offers

ABSTRACT

Prescreened credit card offers, such as offers for credit cards that a particular potential borrower is likely to be granted upon completion of a full application, are ranked based on expected values of respective prescreened offers. The expected value of a prescreened credit card offer may represent an expected monetary value to one or more referrers involved in providing the prescreened offer to the borrower. Thus, the referrer may present a highest ranked credit card offer to a potential borrower first in order to increase the likelihood that borrower applies for the credit card offer with the highest expected value to the referrer. Depending on the embodiment, the expected value of a credit card offer may be based on a combination of a bounty associated with the offer, a click-through-rate for the offer, and/or a conversion rate for the offer, for example.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/824,252, filed Aug. 31, 2006, which is hereby incorporated byreference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to systems and methods of ranking prescreenedcredit offers.

2. Description of the Related Art

Lending institutions provide credit accounts such as mortgages,automobile loans, credit card accounts, and the like, to consumers.Prior to providing an account to a potential borrower, however, many ofthese institutions review credit related data, demographic data, and/orother data related to the potential borrower in order to determinewhether the borrower should be issued the applied-for credit account. Inthe case of credit cards, for example, credit card issuers typicallyobtain a credit report for the potential borrower in order to aid indetermining whether the borrower should be offered a credit card and, ifso, what rates and terms should be offered to the borrower. Thus, forany particular credit card, a first group of borrowers will be acceptedfor the credit card and a second group of borrowers will not be acceptedfor the credit card, where the size of the accepted group typicallyincreases as the desirability of the credit card decreases.

Certain lenders, such as credit card issuers, for example, offer a“finder's fee,” also referred to herein as a “bounty,” to an entity thatrefers a potential borrower to apply for a particular credit card,should the borrower eventually be issued the particular credit card.Thus, some businesses present invitations to apply for credit cards totheir customers, hoping that some customers will click on the invitationto apply, fill out an application for the credit card, and eventually beissued a credit card.

SUMMARY

In one embodiment, a computerized system for presenting prescreenedcredit card offers to a borrower comprises a prescreen module configuredto receive an indication of one or more prescreened credit card offersfor a borrower, wherein the borrower has at least about a 90% likelihoodof being granted a credit card associated with each of the prescreenedcredit card offers after completing a corresponding full credit cardapplication, a ranking module configured to assign a unique rank to atleast some of the prescreened credit card offers, wherein determinationof respective ranks for the prescreened credit card offers is based onat least a bounty and a click-thru-rate associated with respectiveprescreened credit card offers, and a presentation module configured togenerate a data structure comprising information regarding at least ahighest ranked credit card offer.

In one embodiment, a method of determining prescreened credit cardoffers comprises receiving information regarding a borrower from areferring website, determining two or more prescreened credit cardoffers associated with the borrower, determining ranking criteriaassociated with the referring website, the ranking criteria comprisingan indication of attributes associated with one or more of the borrowerand respective prescreened credit card offers, calculating an expectedvalue of the two or more prescreened credit card offers based at leaston the attributes indicated in the ranking criteria, and transmitting adata file to the referring website, the data file comprising anidentifier of one of the prescreened credit card offers having anexpected value higher than the expected values of the other prescreenedcredit card offers.

In one embodiment, a method of ranking a plurality of credit card offersthat have been prescreened for presentation to a potential borrowercomprises receiving information regarding each of a plurality of creditcard offers, determining an expected value of each of the prescreenedcredit card offers, wherein the expected value for a particular creditcard offer is based on at least (1) a money amount payable to a referrerif the potential borrower is issued a particular credit card associatedwith the particular credit card offer; (2) an expected ratio ofpotential borrowers that will apply for the particular credit card offerin response to being presented with the particular credit card offer,and (3) an expected ratio of potential borrowers that will be issued theparticular credit card associated with the particular credit card offer,and ranking the plurality of credit card offers based on the expectedvalues for the respective credit card offers.

In one embodiment, a method of determining an expected value for each ofa plurality of credit card offers comprises receiving an indication of aplurality of prescreened credit card offers associated with anindividual, receiving an indication of a plurality of attributesassociated with each of the prescreened credit card offers, andcalculating an expected value for each of the prescreened credit cardoffers using at least two of the plurality of attributes for eachrespective prescreened credit card offer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one embodiment of a computing deviceconfigured to rank prescreened credit card offers.

FIG. 2 is a flowchart illustrating one embodiment of a process that maybe performed by the computing device of FIG. 1 in order to receiveprescreened offers and rank those prescreened offers.

FIG. 3 is a flowchart illustrating one embodiment of a process ofranking prescreened offers.

FIG. 4 is one embodiment of a user interface that allows a potentialborrower to enter information for submission to a prescreen provider.

FIG. 5 is one embodiment of a user interface presenting a highest rankedprescreened credit card offer to a particular borrower.

FIG. 6 is one embodiment of a user interface presenting a second highestranked prescreened credit card offer to the particular borrower.

FIG. 7 is one embodiment of a user interface that may be presented to avisitor of a third party website, such as a website that offers goodsand/or services to visitors, providing the visitor with an opportunityto apply for a highest ranked prescreened credit card offer.

FIG. 8 is one embodiment of a user interface for presenting multipleranked, prescreened credit card offers to a borrower, along withrespective links associated with the offers that may be selected inorder to apply for one or more of the prescreened credit cards.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

Embodiments of the invention will now be described with reference to theaccompanying Figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive manner,simply because it is being utilized in conjunction with a detaileddescription of certain specific embodiments of the invention.Furthermore, embodiments of the invention may include several novelfeatures, no single one of which is solely responsible for its desirableattributes or which is essential to practicing the inventions describedherein.

The systems and methods described herein perform a prescreening processon a potential borrower to determine which available credit cards theborrower will likely be issued after completing a full application withthe issuer. The term “potential borrower,” or simply “borrower,”includes one or more of a single individual, a group of people, such asa couple or a family, or a business. The term “prescreened credit cardoffers,” “prescreened offers,” or “matching offers,” refers to zero ormore credit card offers for which a potential borrower will likely beapproved by the issuer, where the prescreening process may be based oncredit data associated with the borrower, as well as approval rules fora particular credit card and/or credit card issuer, and any otherrelated characteristics. In one embodiment, a particular credit cardoffer is included in prescreened credit card offers for a particularborrower if the likelihood that the borrower will be granted theparticular credit card offer, after completion of a full application, isgreater than a predetermined threshold, such as 60%, 70%, 80%, 90%, or95%, for example.

In one embodiment, the prescreened offers are ranked, such as byassigning a 1-N ranking to each of N prescreened offers for a particularborrower, where N is the total number of prescreened offers for aparticular borrower. In one embodiment, the rankings are generally basedupon a bounty paid to the referrer. In another embodiment, the rankingsare based on an expected value of each prescreened offer, whichgenerally represents an expected monetary value to one or more referrersinvolved in providing the prescreened offer to the borrower. In oneembodiment, the expected value of a credit card offer is based on abounty associated with the offer, a click-through-rate for the offer,and/or a conversion rate for the offer. In another embodiment, theexpected value for a credit card offer may be based on fewer or moreattributes. Exemplary systems and methods for determining expectedvalues and corresponding rankings for prescreened credit card offers aredescribed below.

FIG. 1 is block diagram of a prescreened credit card offer rankingdevice 100, or simply a “ranking device 100,” configured to rankprescreened credit card offers. The exemplary ranking device 100 is incommunication with a network 160 and various devices and data sourcesare also in communication with the network 160. In the embodiment ofFIG. 1, a prescreen device 162 and a borrower device 164, such as acomputing device executing a web browser, and a third party data source166 are each in communication with the network 160. The ranking device100 may be used to implement certain systems and methods describedherein. For example, in one embodiment the ranking device 100 may beconfigured to prescreen potential borrowers in order to receive a listof prescreened credit card offers and rank the prescreened offers forpresentation to the borrower. In certain embodiments, the ranking device100 also performs portions of the prescreening process that results in alist of unranked prescreened offers, prior to ranking the prescreenedoffers. In other embodiments, the ranking device 100 receivesprescreened offers from a networked device. The functionality providedfor in the components and modules of the ranking device 100 may becombined into fewer components and modules or further separated intoadditional components and modules.

In general, the word module, as used herein, refers to logic embodied inhardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software instructions may be embedded in firmware, such asan EPROM. It will be further appreciated that hardware modules may becomprised of connected logic units, such as gates and flip-flops, and/ormay be comprised of programmable units, such as programmable gate arraysor processors. The modules described herein are preferably implementedas software modules, but may be represented in hardware or firmware.Generally, the modules described herein refer to logical modules thatmay be combined with other modules or divided into sub-modules despitetheir physical organization or storage.

In one embodiment, the ranking device 100 includes, for example, aserver or a personal computer that is IBM, Macintosh, or Linux/Unixcompatible. In another embodiment, the ranking device 100 comprises alaptop computer, cellphone, personal digital assistant, kiosk, or audioplayer, for example. In one embodiment, the exemplary ranking device 100includes a central processing unit (“CPU”) 105, which may include aconventional microprocessor. The ranking device 100 further includes amemory 130, such as random access memory (“RAM”) for temporary storageof information and a read only memory (“ROM”) for permanent storage ofinformation, and a mass storage device 120, such as a hard drive,diskette, or optical media storage device. Typically, the modules of theranking device 100 are connected to the computer using a standards basedbus system. In different embodiments, the standards based bus systemcould be Peripheral Component Interconnect (PCI), Microchannel, SCSI,Industrial Standard Architecture (ISA) and Extended ISA (EISA)architectures, for example.

The ranking device 100 is generally controlled and coordinated byoperating system software, such as the Windows 95, 98, NT, 2000, XP,Linux, SunOS, Solaris, PalmOS, Blackberry OS, or other compatibleoperating systems. In Macintosh systems, the operating system may be anyavailable operating system, such as MAC OS X. In other embodiments, theranking device 100 may be controlled by a proprietary operating system.Conventional operating systems control and schedule computer processesfor execution, perform memory management, provide file system,networking, and I/O services, and provide a user interface, such as agraphical user interface (“GUI”), among other things.

The exemplary ranking device 100 includes one or more commonly availableinput/output (I/O) devices and interfaces 110, such as a keyboard,mouse, touchpad, and printer. In one embodiment, the I/O devices andinterfaces 110 include one or more display device, such as a monitor,that allows the visual presentation of data to a user. Moreparticularly, a display device provides for the presentation of GUIs,application software data, and multimedia presentations, for example.The ranking device 100 may also include one or more multimedia devices140, such as speakers, video cards, graphics accelerators, andmicrophones, for example.

In the embodiment of FIG. 1, the I/O devices and interfaces 110 providea communication interface to various external devices. In the embodimentof FIG. 1, the ranking device 100 is in communication with a network160, such as any combination of one or more LANs, WANs, or the Internet,for example, via a wired, wireless, or combination of wired andwireless, via the communication link 115. The network 160 communicateswith various computing devices and/or other electronic devices via wiredor wireless communication links. In the exemplary embodiment of FIG. 1,the network 160 is in communication with the prescreen device 162, whichmay comprise a computing device and/or a prescreen data store operatedby a credit bureau, bank, or other entity. For example, in oneembodiment the prescreen device 162 comprises a prescreen data storecomprising credit related data for a plurality of individuals. In oneembodiment, the prescreen device 162 also comprises a computing devicethat determines one or more prescreened offers for borrowers andprovides the prescreened offers directly to the borrower or to theranking device 100, for example.

The borrower 164, also in communication with the network, may sendinformation to the ranking device 100 via the network 160 via a websitethat interfaces with the ranking device 100. Depending on theembodiment, information regarding a borrower may be provided to theranking device 100 from a website that is controlled by the operator ofthe ranking device 100 (referred to generally as the “ranking provider”)or from a third party website, such as a commercial website that sellsgoods and/or services to visitors. The third party data source 166 maycomprise any number of data sources, including web sites and customerdatabases of third party websites, storing information regardingpotential borrowers. As described in further detail below, the rankingdevice 100 receives information regarding a potential borrower directlyfrom the borrower 164 via a website controlled by the ranking provider,from the third party data source 166, and/or from the prescreen device162. Depending on the embodiment, the ranking device 100 eitherinitiates a prescreen process, performs a prescreen process, or simplyreceives prescreened offers for a borrower from the prescreen device162, for example, prior to ranking the prescreened offers.

In the embodiment of FIG. 1, the ranking device 100 also includes threeapplication modules that may be executed by the CPU 105. Moreparticularly, the application modules include a prescreen module 130, aranking module 150, and a presentation module 170, which are discussedin further detail below. Each of these application modules may include,by way of example, components, such as software components,object-oriented software components, class components and taskcomponents, processes, functions, attributes, procedures, subroutines,segments of program code, drivers, firmware, microcode, circuitry, data,databases, data structures, tables, arrays, and variables.

FIG. 2 is a flowchart illustrating an exemplary process that may beperformed by the ranking device 100 (FIG. 1) or other suitable computingdevice in order to rank prescreened offers for borrowers. Depending onthe embodiment, certain of the blocks described below may be removed,others may be added, and the sequence of the blocks may be altered. Forexample, in one embodiment the process may begin with block 230 wherethe ranking device 100 receives prescreened offers for a borrower from aprescreen device 162 without being previously involved in theprescreening process.

Beginning in block 210, the prescreen module 130 (FIG. 1) of the rankingdevice 100 receives, or otherwise accesses, information regarding apotential borrower. For example, the prescreen module 130 may receiveinformation, such as a name and address of the borrower 164, that hasbeen entered into a website that is dedicated to matching consumers toprescreened credit card offers. In one embodiment, the borrower 164operates a computing device comprising a browser that is configured torender a web interface provided by the ranking entity or an affiliate ofthe ranking entity, such as an entity that performs the prescreening ofborrowers. In this embodiment, the borrower may enter data into the userinterface specifically for the purpose of being presented with one ormore prescreened offers. In another embodiment, the borrower informationmay be received from another borrower data source 162, such as acommercial website that wants to provide customers with one or moreprescreened credit card offers. For example, a third party website thatsells products and/or services to customers may send borrower data tothe ranking device 100 in order to receive prescreened offers that maybe presented to their customers. As noted above, in one embodiment thereferrer of a borrower to apply for a credit card may receive a bountyupon issuance of an applied-for credit card to the customer. Thus, ifthe borrower information is received from the borrower device 164 via awebsite operated by the ranking entity, the bounty may be paid to theranking entity. Likewise, if borrower information is provided by a thirdpart, such as from the third party data source 166, a portion or all ofthe bounty may be paid to the third party. In other embodiments, thebounty could be shared between one or more third parties, the rankingentity, the prescreen entity, and/or others involved in the prescreeningand ranking processes. In some embodiments, certain or all of theprescreened offers are not associated with a bounty.

Moving to block 220, the prescreen module 130 (FIG. 1) then performs theprescreen process, requests that a third party, such as the prescreendevice 162, performs the prescreen process, or simply receivesprescreened credit card offers from the prescreen device 162, forexample. In one embodiment, the prescreen process accesses creditrelated data regarding the borrower and/or lender criteria associatedwith each of a plurality of credit card offers in order to determine oneor more credit card offers that the borrower would likely be eligiblefor. Co-pending U.S. patent application Ser. No. 11/537,330, titled“Online Credit Card Prescreen Systems And Methods,” filed on Sep. 29,2006, which is hereby incorporated by references in its entirety,describes various methods of determining prescreened credit card offersfor a potential borrower.

Continuing to block 230, the prescreened offers are received by theprescreen module 130, such as from the prescreen device 162.Alternatively, in an embodiment where the prescreen module 130 performsthe prescreen process, in block 230 the prescreen module 130 completesthe prescreen process and makes the prescreened offers available toother modules of the ranking device 100.

Moving to block 240, information regarding the prescreened offers isaccessed by the ranking module 150. As described in further detail belowwith reference to FIG. 3, for example, the ranking module 150 ranks theprescreened offers according to one or more attributes. The attributesmay be borrower attributes, attributes associated with particularprescreened offers, credit card issuer attributes, and/or other relevantattributes. In one embodiment, the attributes used by the ranking module150, and the relative weightings assigned to each of the usedattributes, are determined by the ranking entity and/or by a third partyreferrer that presents the ranked prescreened offers to the borrower.

Next, in block 250, the ranked prescreened offers are accessed by thepresentation module 170 (FIG. 1), which is configured to make theprescreened offers available to the borrower, such as via a websiteoperated by the prescreen provider, the ranking provider, and/or a thirdparty. In one embodiment, for example, the presentation module 170generates a presentation interface, such as one or more HTML pages, forexample, that includes indications of one or more of the ranked creditcard offers. Depending on the embodiment, the presentation interface maybe rendered in a web browser, a portable document file viewer, or anyother suitable file viewer. In one embodiment, the presentationinterface comprises an embedded viewer so that the prescreened offersmay be viewed without the need for a host viewing application on theborrower's computing device. Additionally, the presentation interfacemay comprise software code configured for rendering in a portable devicebrowser, such as a cell phone or PDA browser, or other application on aportable device.

In one embodiment, the presentation interface comprises informationregarding only the highest ranked prescreened offer. In otherembodiments, the presentation interface comprises information regardingmultiple, or all, of the prescreened offers and an indication of therespective offer rankings. Depending on the embodiment, the presentationinterface may comprise software code that depicts one or more of theranked credit card offers on individual pages in sequence, in a verticallist on a single page, and/or in a flipbook type flash-based viewer, forexample. In other embodiments, the presentation interface comprises anyother suitable software code for displaying the ranked credit cardoffers to the borrower or raw data that is usable for generating a userinterface for presentation to the borrower. FIG. 2 illustrates only oneembodiment of a method that may be used to rank prescreened offers.

FIG. 3 is a flowchart illustrating one embodiment of a process ofranking prescreened offers, such as may be performed in block 240 ofFIG. 2. In an advantageous embodiment, multiple credit card offers thatare returned from (or received by) the prescreen module 130, possiblyfrom multiple credit card issuers, are presented to the borrower. Inthis embodiment, the ranking module 150 may rank the prescreened offersaccording to one or more attributes of the particular prescreenedoffers, credit card issuer criteria, and/or borrower characteristics,for example.

Beginning in block 310, the ranking module 150 determines the attributesto be considered in the ranking process. Additionally, the rankingmodule 150 may determine weightings that should be assigned toattributes, if any. In one embodiment, attribute weightings aredetermined based on ranking criteria from the referring entity, such asa third party transmitting borrower data from the third party datasource 166, ranking criteria from the prescreen device 162, and/orranking criteria established by the ranking entity. For example, a firstthird party website may be associated with a first set of rankingcriteria, where the ranking criteria indicate attributes, and possiblyweightings for certain of the attributes, that should be applied toprescreened offers in determining prescreened offer rankings forvisitors of the first third party website. Likewise, a second thirdparty website may have a partially or completely different set ofranking criteria (where the ranking criteria comprises one or moreattributes, and possibly different weightings for certain attributes),that should be applied to prescreened offers in determining prescreenedoffers for visitors of the second third party website. In oneembodiment, if no ranking criteria are provided by the entity requestingthe prescreened offer rankings, no ranking of the prescreened offers isperformed or, alternatively, a default set of ranking criteria may beused to rank the prescreened offers.

For example, borrower data received from a third party data source 162may indicate that bounty is the only attribute to be considered inranking prescreened offers. Thus, if three prescreened offers arereturned from the prescreen module 130 for a particular borrower, andeach offer has a different bounty, the offer with the largest bountywill be ranked highest and, thus, displayed to the borrower first.

Other attributes that may be considered in the prescreen process mayinclude, for example, historical click-through-rate for an offer,historical conversion rate for an offer, geographic location of theborrower, special interests of the borrower, modeled overall clickpropensity for the borrower, the time of day and/or day of week that theprescreening is requested, and promised or desired display rates for anoffer. Each of these terms is defined below:

“Click-through-rate” or “CTR” means the ratio of an expected number oftimes a particular credit card offer will be pursued by borrowers to anumber of times the credit card offer will be displayed to borrowers.Thus, if a credit card offer is expected to be pursued by borrowers 30times out of each 60 times the offer is presented, the CTR for thatoffer is 50%. The CTR may be determined from historical rates ofselection for presented credit card offers.

“Conversion Rate” or “CR” means the expected percentage of borrowersthat will be accepted for a particular credit card upon application forthe credit card. In one embodiment, each credit card offer has anassociated conversion rate. The CR may be determined from historicalrates of borrowers that are accepted for respective credit card offers.

“Geographic location of the borrower” may comprise one or multiplelevels of geographic identifiers associated with a borrower. Forexample, the geographic location of the borrower may indicate theresidential location of the borrower and/or a business location of theborrower. The geographic location of the borrower may further indicate aportion of a municipality, a municipality, a county, a region, a state,or a country in which the borrower resides.

“Click Propensity” means the particular borrower's propensity to selectlinks that are presented to the borrower. In one embodiment, clickpropensity may be limited to certain types of links, such as financerelated links. In one embodiment, click propensity may be determinedbased on historical information regarding the borrower's browsing habitsand/or demographic analysis of the borrower. In one embodiment, eachborrower is associated with a unique click propensity, while in otherembodiments groups of borrowers, such as borrowers in a commongeographic region or using a particular ISP, may have a common clickpropensity.

“Time of day and/or day of week that the prescreening is requested”means the time of day and/or day of week that a prescreening request isreceived by a prescreen provider, a ranking provider, or by a thirdparty website.

“Promised or desired display rates for the offer” may include periodicdisplay quotas for a particular credit card offer, such as may be agreedupon by a prescreen provider and the credit card issuer, for example.

“Special interests” of the borrower include any indications ofpropensities and/or interests of the borrower. Special interests may bedetermined from information received from the borrower, from a thirdparty through which the prescreened offers are being presented to theborrower, and/or from a third party data source. A third party datasource may comprise a data source that may charge a fee for providingdata regarding borrowers, such as interests, purchase habits, and/orlife-stages of the borrower, for example. The special interest data mayindicate, for example, whether the borrower is interested in outdooractivities, travel, investing, automobiles, gardening, collecting,sports, shopping, mail-order shopping, and/or any number of additionalitems. In one embodiment, special interests of the borrower are providedby Experian's Insource data source.

In one embodiment, the ranking process may also comprise determining anexpected value of certain prescreened offers using one or more of theabove attributes and then performing a long term projection thatconsiders offer display limits and schedules imposed by issuers, forexample, as well as expected traffic patterns, in order to rank thecredit card offers.

Thus, prescreened offers may be ranked using ranking criteria comprisingany combination of the above-listed characteristics, and with variousweightings assigned to the attributes. For example, in one embodimentthe ranking module 150 may use ranking criteria that ranks prescreenedoffers based on each of the above-cited attributes that are weighted inthe order listed above, such that the bounty is the most important(highly weighted) attribute, click-through-rate is the second mostimportant attribute, and the promised or desired display rates for theoffer is the least important (lowest weighted) attribute. In otherembodiments, any combination of one or more of the above discussedattributes may be included in ranking criteria.

Moving to block 320, an expected value to the referrer of showing eachprescreened offer to the borrower is determined based upon thedetermined weighted attributes. Finally, in block 330, the prescreenedoffers assigned ranks based on their respective expected values. In oneembodiment, block 330 is bypassed and the expected values for creditcard offers represent the ranking.

Described below are exemplary methods of ranking prescreened offers,such as may be performed in blocks 320, 330 of FIG. 3. The examplesbelow are provided as examples of how ranking may be performed and arenot intended to limit the scope of the systems and methods describedherein. Accordingly, it will be appreciated that other methods ofranking prescreened offers using the above-listed attributes, inaddition to any other available attributes, in various othercombinations and with different weightings than discussed herein, areexpressly contemplated

In one embodiment, rankings may be based on bounty alone. For example,the table below illustrates four prescreened offers that are rankedaccording to bounty.

TABLE 1 Offer Number Bounty($) Rank 1 0.40 3 2 0.30 4 3 1.20 1 4 0.75 2Thus, if the ranking is based only on bounty, the referrer would likelydisplay Offer 3 first, as it has the highest bounty, Offer 4 next,followed by Offer 1, and then Offer 2. In another embodiment, thereferrer may display only a single credit card offer to the borrower ora subset of the offers to the borrower. In this embodiment, the borrowerwould likely display the highest ranked offer.

In another embodiment, the ranking criteria may include one or more of acombination of bounty, click-though-rate, and conversion rate for eachprescreened offer. Considering the same four offers listed in Table 1,when the click-through-rate and conversion rate are also considered, therankings could change significantly. In one embodiment, the bounty issimply multiplied by the click-through-rate and conversion rate in orderto determine an expected value for each offer, where the highestexpected value would be ranked highest. The table below illustrates thefour exemplary prescreened offers illustrated in Table 1, but withrankings that are based on the click-through-rate and conversion rate,as well as the bounty, for each offer.

TABLE 2 Click- Expected through- Conversion Value Rank Offer rate rate(Bounty * [Rank in Number Bounty (percent) (percent) CTR * CR) Table 1]1 0.40 4 0.3 0.48 4[3] 2 0.30 2.5 1.1 0.82 2[4] 3 1.20 1 0.6 0.72 3[1] 40.75 3 1.4 3.15 1[2]The last column of the above table illustrates both the ranking for eachoffer based on a calculated expected value, and also indicates theranking for each prescreened offer based only on bounty [in brackets].As shown, each of the offer rankings has changed. For example, thesecond highest ranked prescreened offer based only on bounty is thehighest ranked prescreened offer based on the expected value, while thehighest ranked prescreened offer based only on bounty is now the thirdhighest ranked prescreened offer.

In another embodiment, the attributes used in determining an expectedvalue of prescreened offers may be weighted differently, such thatcertain heavily weighted factors may have more affect on the expectedvalue than other lower weighted attributes. For example, with regard toTable 2, if the bounty and the conversion rate are the most importantfactors, while the click-through-rate is not as important in determiningan expected value, the bounty and conversion rates may each be weightedhigher by multiplying their values by 2, 3, 4, 5 or some othermultiplier, while not multiplying the click-through-rate by amultiplier, or multiplying the click-through-rate by a fractionalmultiplier, such as 0.9, 0.8, 0.7, 0.5, or lower. Table 3 belowillustrates the four exemplary prescreened offers illustrated above, butwith an exemplary weighting of 2 assigned to the bounty and conversionrate and no weighting assigned to the click-through rate.

TABLE 3 Expected Value (weighted Weighted Click- Bounty * Rank Weightedthrough- Conversion Weighted CTR * [Rank in Offer Bounty Bounty raterate Conversion weighted Table 1, Number ($) (*2) (percent) (percent)rate (*2) CR) Table 2] 1 0.40 0.80 4 0.3 0.6 1.92 4 [4, 3] 2 0.30 0.602.5 1.1 2.2 3.3 3 [2, 4] 3 1.20 2.40 1 0.6 1.2 2.88 2 [3, 1] 4 0.75 1.503 1.4 2.8 12.6 1 [1, 2]In the example of Table 3, the ranking for offers 2 and 3 havealternated when the exemplary weightings for the bounty and theconversion rate were added.

In one embodiment, special interests of the borrower are used incalculating an expected value for certain or all prescreened offers. Forexample, an expected value formula may include a special interest value,where certain credit cards are associated with various special intereststhat increase the special interest value for borrowers that aredetermined to have corresponding special interests. For example, a firstcredit card may be sports related, while a second credit card may have arewards program offering movie tickets to cardholders. Thus, for aborrower with special interests in one or more sports, the specialinterest value for the first card may be increased, such as to 2 or 3,while the special interest value for the same borrower may be 1 or lessfor the second card. In one embodiment, the special interest values varybased on the borrowers strength in a particular interest segment. Forexample, a strong NASCAR fan might have a special interest value of 3for a NASCAR-related credit card, while a weak traveler might only havea special interest value of 1.1 for a travel-related credit card. Inother embodiments, the special interest values may be lower or higherthan the exemplary values described above. The special interests of theborrower may be used in other manners in ranking prescreened offers.

In one embodiment, expected values for prescreened offers include afactor indicating expected future rankings for a respective card.Alternatively, a calculated expected value for a card may be adjustedbased on a determined expected future ranking for the card. For example,the expected future ranking of one or more credit card offers X hours(where X is any number, such as 0.25, 0.5, 1, 2, 4, 8, 12, or 24, forexample) after determining the initial rankings may impact the initialrankings. Thus, rankings for each of a plurality of prescreened offersmay first be generated and then modified based on expected futurerankings for respective offers. For example, prescreened offer rankingsfor a first user determined at a first time, e.g., in the morning, mayinclude multiple prescreened offers ranked according to the prescreenedoffers respective expected values in the order: offer A, E, and D. Inthis embodiment, the expected value for offer A may be only slightlylarger than offer E (or may be significantly larger than offer E). Inone embodiment, after determining the ranking order for the first user,the ranking module 150 analyzes a historical traffic pattern for one ormore of offers A, E, and D, and determines that typically later in theday (e.g., 4-8 hours after the initial prescreening is performed) alarge quantity of borrowers apply for the card associated with offer A,while very few apply for the card associated with offer E. Thus, incertain embodiments offer E may be promoted to the first choice for thefirst user because offer E is even less likely to be applied-for laterin the day. In one embodiment, an expected value formula for a group ofprescreened offers may include an expected future ranking value, wherethe expected future ranking value for the prescreened offers may bedetermined using precalculated trending data or using realtime updatedtrending data. In some embodiments, the expected values for credit cardoffers may be affected by offer presentation limits or quotas associatedwith certain prescreened offers.

FIG. 4 is one embodiment of a user interface 400 that allows a potentialborrower to enter information for submission to a ranking providerand/or to a prescreen provider. In one embodiment, the user interface400 is controlled by the prescreen provider such that data submitted inthe user interface 400 is transmitted to the ranking device 100 (FIG.1). In other embodiments, the user interface 400, or similar interface,may be presented to a borrower by the prescreen provider or by a thirdparty website. For example, the third party data source 166 may comprisesoftware code, such as HTML, CSS, XML, JavaScript, etc., configured torender a user interface, such as the user interface 400, in the browserof the borrower 164.

In the embodiment of FIG. 4, the user interface 400 comprises a firstname and last name field 410, 420, a home address field for 30, a statefield 440, and a zip code field 450, each comprising text entry fieldsin the exemplary user interface 400. Depending on the embodiment, one ormore of the fields 410, 420, 430, 440, 450 may be replaced by other datacontrols, such as drop-down lists, radio buttons, or auto-fill textboxes, for example. In other embodiments, the user interface 400comprises only a subset of the text entry fields illustrated in FIG. 4.For example, in one embodiment the user interface 400 may include only alast name field 420 and a ZIP code field 450. The user interface 400further comprises a start button 460 that is selected in order totransmit entered data to the prescreen provider, the ranking provider,and/or the hosting third party website. In one embodiment, when theborrower selects the start button 460, the borrower data is transmittedto the ranking device 100 and a prescreening and ranking procedure, suchas the method of FIGS. 2 and/or 3, is performed using the borrower data.

FIG. 5 is one embodiment a user interface 500 presenting a credit cardoffer that a first borrower was matched to, along with a link 510 thatmay be selected in order to apply for the illustrated credit card.Exemplary user interface 500 also includes a link 520 that may beselected in order to display one or more additional credit cards towhich the borrower has been matched. In the embodiment of FIG. 5, theuser interface 500 comprises term information 530, overview information532, summary information 534, and a card image 536 for the prescreenedcredit card offer. In one embodiment, the user interface 500 ispresented to the borrower after the borrower completes the text entryfields of a user interface, such as user interface 400, and submits theborrower information, such as by clicking on the start button 460 ofuser interface 400. In other embodiments, a third party website mayprovide borrower information to the prescreen provider and, in response,the prescreen provider may transmit a ranked listing of prescreenedcredit card offers to the third party website, which may be presented tothe borrower via a user interface such as user interface 500.

In the embodiment of FIG. 5, the user interface 500 indicates that theborrower has been prescreened for 4 credit cards, meaning that theprescreening process has indicated that there are 4 credit cards thatthe particular borrower would likely be granted after completion of afull application with the respective issuers. While the prescreen module130 indicates that there are 4 prescreened credit card offers for theparticular borrower, the user interface 500 displays informationregarding only a single highest ranked prescreened credit card offer. Inone embodiment, if the borrower does not care to apply for the displayedhighest ranked prescreened offer, the borrower may select the link 520and be presented with one or more of a second through fourth rankedprescreened offers. As noted above, the prescreened offers may be rankedaccording to various combinations of criteria associated with theborrower, such as the borrowers credit information, Web browsingcharacteristics, geographic location, as well as criteria established bythe respective credit card issuers, among other attributes.

FIG. 6 is one embodiment of a user interface 600 presenting a secondhighest ranked credit card offer to the borrower in response toselecting the link 520 of FIG. 5, for example. As noted above withrespect to FIG. 5, if the borrower is not interested in applying for thehighest ranked credit card offer presented in the user interface 500,the borrower may select to view another prescreened credit card offer,such as is presented in the user interface 600. The user interface 600comprises a link 610 that may be selected in order to apply for theillustrated (second highest ranked) credit card and a link 620 that maybe selected in order to display one or more additional lower ranked(e.g., third highest ranked) prescreened credit card offers to which theborrower. Similar to the user interface 500, the user interface 600 alsocomprises term information 630, overview information 632, summaryinformation 634, and a card image 636 for the illustrated prescreenedcredit card offer. Depending on the embodiment, the borrower is notaware of any special ordering of the credit card offers that arepresented.

FIG. 7 is one embodiment of a user interface 700 that may be presentedto a visitor of a third party website, such as a website that offersgoods and/or services to visitors. For example, a user interface similarto that of FIG. 7 may be presented to a visitor of a shopping websiteafter the visitor has selected one or more products for purchase and hasselected a “checkout” or “complete transaction” link on the shoppingwebsite. The exemplary user-interface 700 comprises information 710regarding a highest ranked prescreened credit card offer for theparticular visitor, as determined by the ranking device 100, forexample, via one or more network connections, such as the network 160.In one embodiment, the third party website requests visitor informationthat is used in locating prescreened credit card offers for the visitorprior to presenting the user interface 700. In one embodiment, the thirdparty website comprises a customer database that contains visitorinformation that was received during a previous visit to the third partywebsite by the visitor. Thus, in one embodiment the visitor is notrequested to supply personal information, but instead the third partywebsite locates the visitor information and provides the information tothe ranking device 100.

In the embodiment of FIG. 7, the borrower can apply for the prescreenedcredit card by selecting the start button 720 of user interface 700. Inone embodiment, when the start button 720 is selected by the borrower, auser interface from the credit card issuer, or an agent of the creditcard issuer, is provided to the borrower in order to complete the creditcard application process. In one embodiment, after completing theapplication process with the credit card issuer, the borrower is able touse the new credit card for purchase of the goods and/or services fromthe third party website.

In the embodiment of FIG. 7, the visitor to the third party website maychoose to view additional prescreened offers by selecting the link 730,in response to which the visitor is provided with additional prescreenedcredit card offers in an order that is determined by the rankings forthe respective offers. For example, the visitor may be presented with auser interface including data regarding a second highest ranked creditcard offer.

FIG. 8 is one embodiment of a user interface 800 for presenting multiplecredit card offers that a borrower was matched to, along with respectivelinks associated with the offers that may be selected in order to applyfor a credit card. In the embodiment of FIG. 8, a top threehighest-ranked prescreened offers are simultaneously displayed to theborrower in the user interface 800. In one embodiment, the top threeranked prescreened offers are the three credit card offers with thehighest calculated expected values. As discussed above, the expectedvalues for respective credit card offers may be calculated based onvarious combinations of attributes and possibly weightings forrespective attributes. In certain embodiments, the host of the interface800, such as the ranking provider or a third party website, may select acombination of attributes to be used in calculating expected values foravailable prescreened credit card offers

In one embodiment, the prescreened offer 810 is associated with ahighest ranked prescreened offer, the offer 820 is associate with asecond highest ranked prescreened offer, and the offer 830 is associatedwith a third-highest prescreened offer. In another embodiment, thehighest-ranked prescreened offer is displayed as offer 820, such thatthe highest ranked offer is in a more central portion of the userinterface 800. In this embodiment, the second-highest rank prescreenoffer may be presented as offer 810, and the third-ranked prescreenoffer may be presented as offer 830. The user interface 800 alsocomprises start buttons 812, 814, and 816 that may be selected in orderto initiate application for respective of the prescreened offers 810,820, 830 by the borrower.

The foregoing description details certain embodiments of the invention.It will be appreciated, however, that no matter how detailed theforegoing appears in text, the invention can be practiced in many ways.The use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

What is claimed is:
 1. A method of determining prescreened credit cardoffers, the method comprising: receiving, by a ranking computing device,information regarding a borrower; determining, by the ranking computingdevice, a plurality of prescreened credit card offers associated withthe borrower; receiving, from a third-party computing device operated byan entity controlling operations of a commercial website,entity-specific ranking criteria indicating one or more attributes fordetermining a ranking order of prescreened credit card offers presentedto borrowers visiting the commercial website, the entity-specificranking criteria including criteria for one or more attributes selectedfrom the group comprising a bounty, a click-through rate or a conversionrate, wherein the entity-specific criteria are defined by arepresentative of the entity; calculating, by the ranking computingdevice, a plurality of expected monetary values of respectiveprescreened credit card offers, wherein the expected monetary values arebased on at least one of the bounty, the click-through rate or theconversion rate of the respective credit card offer as indicated in theentity-specific ranking criteria ranking, by the ranking computingdevice, the plurality of prescreened credit card offers based on thecalculated expected monetary values; determining a plurality of futureranking values of respective prescreened credit card offers indicatingprobable future rankings of the respective prescreened credit cardoffers after at least one of the plurality of prescreened credit cardoffers is presented to one or more additional borrowers, wherein thefuture ranking values are based on at least trending data of therespective prescreened credit card offers; adjusting the ranking basedat least in part on the determined future ranking values of respectiveprescreened credit card offers; and transmitting, by the rankingcomputing device, a data file to the third-party computing device, thedata file comprising an identifier of a prescreened credit card offer inthe plurality of prescreened credit card offers that is ranked highestby the adjusted ranking.
 2. The method of claim 1, wherein theentity-specific ranking criteria further comprises a plurality ofmultipliers associated with respective attributes, wherein themultipliers indicates relative weightings of the respective attributes.3. The method of claim 1, wherein the further comprise one or more of: ahistorical click-through-rate, a historical conversion rate, ageographic location of the borrower, a modeled overall click propensityfor the borrower, a time of day, a day of week, and a desired displayrate.
 4. The method of claim 1, wherein the data file further comprisesidentifiers of one or more prescreened credit card offers that areranked lower than the highest ranked prescreened credit card offer. 5.The method of claim 1, wherein at least one of the plurality ofprescreened credit card offers is presented to one or more additionalborrowers within 1, 2, 4, 8, 12, or 24 hours from when the calculatingis performed.
 6. The method of claim 1, wherein the trending datacomprises pre-calculated trending data of the respective prescreenedcredit card offers.
 7. The method of claim 1, wherein the future rankingvalues are further based on presentation limits of the respectiveprescreened credit card offers.
 8. A computing system for determiningprescreened credit card offers, the system comprising: a processor, acomputer readable medium storing machine-executable instructionsincluding one or more modules configured for execution by the processorin order to cause the computing system to: receive information regardinga borrower; determine a plurality of prescreened credit card offersassociated with the borrower; receive, from a third-party computingdevice operated by an entity controlling operations of a commercialwebsite, entity-specific ranking criteria indicating one or moreattributes for determining a ranking order of prescreened credit cardoffers presented to borrowers visiting the commercial website, theentity-specific ranking criteria including criteria for one or moreattributes selected from the group comprising a bounty, a click-throughrate or a conversion rate, wherein the entity-specific criteria aredefined by a representative of the entity; calculate a plurality ofexpected monetary values of respective prescreened credit card offers,wherein the expected monetary values are based on a least one of thebounty, the click-through rate or the conversion rate of the respectivecredit card offer as indicated in the entity-specific ranking criteria;rank the plurality of prescreened credit card offers based on thecalculated expected monetary values; determine a plurality of futureranking values of respective prescreened credit card offers indicatingprobable future rankings of the respective prescreened credit cardoffers after at least one of the plurality of prescreened credit cardoffers is presented to one or more additional borrowers, wherein thefuture ranking values are based on at least trending data of therespective prescreened credit card offers; adjust the ranking based atleast in part on the determined future ranking values of respectiveprescreened credit card offers; transmit a data file to the third-partycomputing device the data file comprising an identifier of a prescreenedcredit card offer in the plurality of prescreened credit card offersthat is ranked the highest by the adjusted ranking.
 9. The system ofclaim 8, wherein the entity-specific ranking criteria further comprisesa plurality of multipliers associated with respective attributes,wherein the multipliers indicate relative weightings of the respectiveattributes.
 10. The system of claim 8 wherein the attributes furthercomprise one or more of a historical click-through-rate, a historicalconversion rate, a geographic location of the borrower, a modeledoverall click propensity for the borrower, a time of day, a day of week,and a desired display rate.
 11. The system of claim 8, wherein the datafile further comprises identifiers of one or more prescreened creditcard offers that are ranked lower than the highest ranked prescreenedcredit card offer.
 12. The system of claim 8, wherein at least one ofthe plurality of prescreened credit card offers is presented to one ormore additional borrowers within 1, 2, 4, 8, 12, or 24 hours from whenthe calculating is performed.
 13. The system of claim 8, wherein thetrending data comprises pre-calculated trending data of the respectiveprescreened credit card offers.
 14. The system of claim 8, wherein thefuture ranking values are further based on presentation limits of therespective prescreened credit card offers.